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## Description We are submitting as a team of four for a project. Other team members are @RuofanChen03, @LikeWang10067, @TANYAL77. This pull requests expands the filtering capabilities of the FAISS vectorstore by adding MongoDB-style query operators indicated as follows, while including comprehensive testing for the added functionality. - $eq (equals) - $neq (not equals) - $gt (greater than) - $lt (less than) - $gte (greater than or equal) - $lte (less than or equal) - $in (membership in list) - $nin (not in list) - $and (all conditions must match) - $or (any condition must match) - $not (negation of condition) ## Issue This closes https://github.com/langchain-ai/langchain/issues/26379. ## Sample Usage ```python import faiss import asyncio from langchain_community.vectorstores import FAISS from langchain.schema import Document from langchain_huggingface import HuggingFaceEmbeddings embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-mpnet-base-v2") documents = [ Document(page_content="Process customer refund request", metadata={"schema_type": "financial", "handler_type": "refund",}), Document(page_content="Update customer shipping address", metadata={"schema_type": "customer", "handler_type": "update",}), Document(page_content="Process payment transaction", metadata={"schema_type": "financial", "handler_type": "payment",}), Document(page_content="Handle customer complaint", metadata={"schema_type": "customer","handler_type": "complaint",}), Document(page_content="Process invoice payment", metadata={"schema_type": "financial","handler_type": "payment",}) ] async def search(vectorstore, query, schema_type, handler_type, k=2): schema_filter = {"schema_type": {"$eq": schema_type}} handler_filter = {"handler_type": {"$eq": handler_type}} combined_filter = { "$and": [ schema_filter, handler_filter, ] } base_retriever = vectorstore.as_retriever( search_kwargs={"k":k, "filter":combined_filter} ) return await base_retriever.ainvoke(query) async def main(): vectorstore = FAISS.from_texts( texts=[doc.page_content for doc in documents], embedding=embeddings, metadatas=[doc.metadata for doc in documents] ) def printt(title, documents): print(title) if not documents: print("\tNo documents found.") return for doc in documents: print(f"\t{doc.page_content}. {doc.metadata}") printt("Documents:", documents) printt('\nquery="process payment", schema_type="financial", handler_type="payment":', await search(vectorstore, query="process payment", schema_type="financial", handler_type="payment", k=2)) printt('\nquery="customer update", schema_type="customer", handler_type="update":', await search(vectorstore, query="customer update", schema_type="customer", handler_type="update", k=2)) printt('\nquery="refund process", schema_type="financial", handler_type="refund":', await search(vectorstore, query="refund process", schema_type="financial", handler_type="refund", k=2)) printt('\nquery="refund process", schema_type="financial", handler_type="foobar":', await search(vectorstore, query="refund process", schema_type="financial", handler_type="foobar", k=2)) print() if __name__ == "__main__":asyncio.run(main()) ``` ## Output ``` Documents: Process customer refund request. {'schema_type': 'financial', 'handler_type': 'refund'} Update customer shipping address. {'schema_type': 'customer', 'handler_type': 'update'} Process payment transaction. {'schema_type': 'financial', 'handler_type': 'payment'} Handle customer complaint. {'schema_type': 'customer', 'handler_type': 'complaint'} Process invoice payment. {'schema_type': 'financial', 'handler_type': 'payment'} query="process payment", schema_type="financial", handler_type="payment": Process payment transaction. {'schema_type': 'financial', 'handler_type': 'payment'} Process invoice payment. {'schema_type': 'financial', 'handler_type': 'payment'} query="customer update", schema_type="customer", handler_type="update": Update customer shipping address. {'schema_type': 'customer', 'handler_type': 'update'} query="refund process", schema_type="financial", handler_type="refund": Process customer refund request. {'schema_type': 'financial', 'handler_type': 'refund'} query="refund process", schema_type="financial", handler_type="foobar": No documents found. ``` --------- Co-authored-by: ruofan chen <ruofan.is.awesome@gmail.com> Co-authored-by: RickyCowboy <like.wang@mail.utoronto.ca> Co-authored-by: Shanni Li <tanya.li@mail.utoronto.ca> Co-authored-by: RuofanChen03 <114096642+ruofanchen03@users.noreply.github.com> Co-authored-by: Like Wang <102838708+likewang10067@users.noreply.github.com>
🦜️🧑🤝🧑 LangChain Community
Quick Install
pip install langchain-community
What is it?
LangChain Community contains third-party integrations that implement the base interfaces defined in LangChain Core, making them ready-to-use in any LangChain application.
For full documentation see the API reference.
📕 Releases & Versioning
langchain-community is currently on version 0.0.x
All changes will be accompanied by a patch version increase.
💁 Contributing
As an open-source project in a rapidly developing field, we are extremely open to contributions, whether it be in the form of a new feature, improved infrastructure, or better documentation.
For detailed information on how to contribute, see the Contributing Guide.